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Multi-microgrid optimization and energy management under boost voltage converter with Markov prediction chain and dynamic decision algorithm

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  • Shayan, Mostafa Esmaeili
  • Najafi, Gholamhassan
  • Ghobadian, Barat
  • Gorjian, Shiva
  • Mamat, Rizalman
  • Ghazali, Mohd Fairusham

Abstract

Knowledge on operational energy consumption and embodied energy, replacing embodied greenhouse gas (GHG) emissions in building materials, and national energy resources have become a necessity. In this study, the importance of investigating the stochastic nature of weather-dependent renewable energies is well documented. The management of the hybrid renewable energy system (HRES) was built and assessed, utilizing a decision-making algorithm and 13 case studies. When the proportion of renewable energy is at 24% and the average daily fossil fuel usage is 1.11 L per year, the HRES generates 1697 kWh per year with a net present value (NPV) of 553,68 USD, with a rate of return (IRR) of 21.4%, and a payback period (PP) of 15.7 years. With a renewable energy share of 54%, fossil fuel consumption dropped to 0.69 L per year, while yearly energy output was comparable to 1652 kWh per year, with an IRR of 19.5% and a PP of 17.6 years. To achieve zero greenhouse gas emissions, HRES Management employs 100% renewable energy sources to generate 1933 kWh per year at a net present value of −372.09 USD. This scenario is economically possible if the renewable energy feed-in tariff exceeds 0.06 USD.

Suggested Citation

  • Shayan, Mostafa Esmaeili & Najafi, Gholamhassan & Ghobadian, Barat & Gorjian, Shiva & Mamat, Rizalman & Ghazali, Mohd Fairusham, 2022. "Multi-microgrid optimization and energy management under boost voltage converter with Markov prediction chain and dynamic decision algorithm," Renewable Energy, Elsevier, vol. 201(P2), pages 179-189.
    • Handle: RePEc:eee:renene:v:201:y:2022:i:p2:p:179-189
    DOI: 10.1016/j.renene.2022.11.006
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